The Influence of the Thermal Storage on the Electricity Production in a Co-Generation in Peak and Off-Peak Time Range

Soročins, Aleksandrs; Rusovs, Dmitrijs; Nagla, Jānis; Žentiņš, Varis

doi:10.1109/rtucon51174.2020.9316576

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…These networks depend heavily on renewable sources and sustainable technologies, when their designs are optimized for cost and CO 2 reduction. To overcome the challenges of meeting the demand for heating and cooling, different approaches are considered at the district heating level, such as scheduling multiple boilers with thermal energy storage systems (TESS) [1], [2], coupling TESS of different volumes into the heat distribution system [3], reducing Thanks to SWECO for providing the data for the case scenario. the power curtailment of wind power through combined heat and power (CHP) units and store the energy into TESS [4], and creating multi-energy systems with distributed energy sources and energy storage systems throughout the network [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing of a Community Level Thermal Energy Storage System

Geraedts

Alpízar-Castillo

Ramírez-Elizondo

et al. 2022

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)

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Fifth-generation energy networks are combined networks of heat and electricity, that have the ability to generate, distribute, store and share energy between consumers. Knowledge on the dynamic behaviour of the physical phenomena related to energy generation, distribution and storage provides insight into the performance of the system as a whole. A mixed-integer linear algorithm is proposed, implementing a partitioned clustering program for subsequent classification of typical demand, grouping specific days with similar demand profiles together. From this arrangement, the optimal network configuration can be determined using an objective function, minimizing the economic and environmental impact. To validate the optimization results, a simulation of the network was built, which mimics its physical dynamic behaviour, and through which the distribution and storage capabilities of the network can be assessed. Advanced advice on fifth-generation energy networks is presented that can be applied to early-stage network design, reducing costs and emissions, along with data on the implementation of renewable energy technologies and their performance. Additionally, this research provides the foundation for numerical modelling of such energy networks which contributes to future research.

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Section: Introductionmentioning

confidence: 99%

Optimal Sizing of a Community Level Thermal Energy Storage System

Geraedts

Alpízar-Castillo

Ramírez-Elizondo

et al. 2022

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)

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“…During the last few years, at least three heat energy storage sites integrated with CHPPs were constructed in Latvia, summarised in Table 1. [3] 18000 550 150 Salaspils Siltums [4] 8000 418 15/25 Jelgava, Fortum Latvia [5] 5000 170 15 The application of heat storage tanks and utilization of the thermal inertia of consumers (i.e., buildings) can cover disbalance between heat demand in DHS and the request for electrical energy produced by CHPPs from the perspective of the electricity wholesale market [6]- [8]. DHS renovation together with the already ongoing pre-insulated network pipe exchange and building thermal insulation improvements will enhance the overall operational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Heat Load Numerical Prediction for District Heating System Operational Control

Rusovs

Jakovleva

Žentiņš

et al. 2021

Latvian Journal of Physics and Technical Sciences

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To develop an advanced control of thermal energy supply for domestic heating, a number of new challenges need to be solved, such as the emerging need to plan operation in accordance with an energy market-based environment. However, to move towards this goal, it is necessary to develop forecasting tools for short- and long-term planning, taking into account data about the operation of existing heating systems. The paper considers the real operational parameters of five different heating networks in Latvia over a period of five years. The application of regression analysis for heating load dependency on ambient temperature results in the formulation of normalized slope for the regression curves of the studied systems. The value of this parameter, the normalized slope, allows describing the performance of particular heating systems. Moreover, a heat load forecasting approach is presented by an application of multiple regression methods. This short-term (day-ahead) forecasting tool is tested on data from a relatively small district heating system with an average load of 20 MW at ambient temperature of 0 °C. The deviations of the actual heat load demand from the one forecasted with various training data set sizes and polynomial orders are evaluated for two testing periods in January of 2018. Forecast accuracy is assessed by two parameters – mean absolute percentage error and normalized mean bias error.

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The Influence of the Thermal Storage on the Electricity Production in a Co-Generation in Peak and Off-Peak Time Range

Cited by 2 publications

References 6 publications

Optimal Sizing of a Community Level Thermal Energy Storage System

Optimal Sizing of a Community Level Thermal Energy Storage System

Heat Load Numerical Prediction for District Heating System Operational Control

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